cse373: data structures & algorithms lecture 6: binary search trees lauren milne summer 2015 1

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CSE373: Data Structures & Algorithms

Lecture 6: Binary Search Trees

Lauren MilneSummer 2015

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Announcements

• HW2 due 10:59 PM Friday• Going to try to rearrange session times.

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Previously on CSE 373

– Dictionary ADT• stores (key, value) pairs• find, insert, delete

– Trees• Terminology• Binary Trees

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Reminder: Tree terminology

A

E

B

D F

C

G

IH

LJ MK N

Node / Vertex

Edges

Root

Leaves

Left subtreeRight subtree

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Example Tree Calculations

A

E

B

D F

C

G

IH

LJ MK N

A

Recall: Height of a tree is the maximum number of edges from the root to a leaf.

Height = 0

A

B Height = 1

What is the height of this tree?

What is the depth of node G?

What is the depth of node L?

Depth = 2

Depth = 4

Height = 4

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Binary Trees• Binary tree: Each node has at most 2 children (branching factor 2)

• Binary tree is• A root (with data)• A left subtree (may be empty) • A right subtree (may be empty)

• Special Cases

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Tree TraversalsA traversal is an order for visiting all the nodes of a tree

• Pre-order: root, left subtree, right subtree

• In-order: left subtree, root, right subtree

• Post-order: left subtree, right subtree, root

+

*

2 4

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(an expression tree)

+ * 2 4 5

2 * 4 + 5

2 4 * 5 +

Binary Search Tree (BST) Data Structure

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• Structure property (binary tree)– Each node has 2 children– Result: keeps operations simple

• Order property– All keys in left subtree smaller

than node’s key– All keys in right subtree larger

than node’s key– Result: easy to find any given key

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A binary search tree is a type of binary tree (but not all binary trees are binary search trees!)

Are these BSTs?

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1171

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Find in BST, Recursive

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Data find(Key key, Node root){ if(root == null) return null; if(key < root.key) return find(key,root.left); if(key > root.key) return find(key,root.right); return root.data;}

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Worst case running time is O(n).- Happens if the tree is very lopsided (e.g. list)

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What is the running time?

Find in BST, Iterative

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307 1710

Data find(Key key, Node root){ while(root != null && root.key != key) { if(key < root.key) root = root.left; else(key > root.key) root = root.right; } if(root == null) return null; return root.data;}

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Worst case running time is O(n).- Happens if the tree is very lopsided (e.g. list)

Bonus: Other BST “Finding” Operations

• FindMin: Find minimum node– Left-most node

• FindMax: Find maximum node– Right-most node 2092

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307 1710

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Insert in BST

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307 17

insert(13)insert(8)insert(31)

(New) insertions happen only at leaves – easy!10

8 31

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Again… worst case running time is O(n), which may happen if the tree is not balanced.

Deletion in BST

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Why might deletion be harder than insertion?

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Deletion in BST

• Basic idea: find the node to be removed, then “fix” the tree so that it is still a binary search tree

• Three potential cases to fix:– Node has no children (leaf)– Node has one child– Node has two children

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Deletion – The Leaf Case

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307 17

delete(17)

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Deletion – The One Child Case

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307 10

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delete(15)

Deletion – The One Child Case

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307 10

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delete(15)

Deletion – The Two Child Case

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What can we replace 5 with?

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delete(5)

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Deletion – The Two Child Case

What can we replace the node with?

Options:• successor minimum node from right subtree

findMin(node.right)

• predecessor maximum node from left subtree findMax(node.left)

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Deletion: The Two Child Case (example)

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1915 3225

delete(23)

Deletion: The Two Child Case (example)

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delete(23)

Deletion: The Two Child Case (example)

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1915 3225

delete(23)

Deletion: The Two Child Case (example)

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3092

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15 3225

Success!

delete(23)

Lazy Deletion

• Lazy deletion can work well for a BST– Simpler– Can do “real deletions” later as a batch– Some inserts can just “undelete” a tree node

• But– Can waste space and slow down find operations– Make some operations more complicated:

• e.g., findMin and findMax?

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BuildTree for BST• Let’s consider buildTree

– Insert all, starting from an empty tree

• Insert keys 1, 2, 3, 4, 5, 6, 7, 8, 9 into an empty BST

– If inserted in given order, what is the tree?

– What big-O runtime for buildTree on this sorted input?

– Is inserting in the reverse order

any better?

1

2

3O(n2)

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BuildTree for BST• Insert keys 1, 2, 3, 4, 5, 6, 7, 8, 9 into an empty BST

• What we if could somehow re-arrange them– median first, then left median, right median, etc.– 5, 3, 7, 2, 1, 4, 8, 6, 9

– What tree does that give us?

– What big-O runtime?

– So the order the values

come in is important!

842

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5

9

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1

O(n log n), definitely better

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